code: def forward(self, x: Tensor, adj_t):
      # h += post_lin(aggr(pre_lin(x), adj))
      # h += lin(x)
      out = self.propagate(adj_t, x=x)
      out += self.lin(x)[: out.size(0)]
      return out
  
imports: from itertools import product
  from typing import Optional, List
  import tqdm
  import torch
  from torch import Tensor
  import torch.nn.functional
  from torch.nn import ModuleList, Linear, BatchNorm1d
  from torch_sparse import SparseTensor
  from torch_geometric.nn import MessagePassing
  from torch_geometric.nn import PNAConv
  from torch_scatter import segment_csr
  from ..models import ScalableGNN
label_desc: Use image node_id 3 for calling the PNAConv obj's underlying member method code with example usage: obj.forward(x, adj_t) and returns: out